Mapping signalling perturbations in myocardial fibrosis via the integrative phosphoproteomic profiling of tissue from diverse sources

Study of the molecular basis of myocardial fibrosis is hampered by limited access to tissues from human patients and by confounding variables associated with sample accessibility, collection, processing and storage. Here, we report an integrative strategy based on mass spectrometry for the phosphoproteomic profiling of normal and fibrotic cardiac tissue obtained from surgical explants from patients with hypertrophic cardiomyopathy, from a transaortic-constriction mouse model of cardiac hypertrophy and fibrosis, and from a heart-on-a-chip model of cardiac fibrosis. We used the integrative approach to map the relative abundance of thousands of proteins, phosphoproteins and phosphorylation sites specific to each tissue source, to identify key signalling pathways driving fibrosis and to screen for anti-fibrotic compounds targeting glycogen synthase kinase 3, which has a consistent role as a key mediator of fibrosis in all three types of tissue specimen. The integrative disease-modelling strategy may reveal new insights into mechanisms of cardiac disease and serve as a test bed for drug screening

Evolutionary histories of breast cancer and related clones

乳がん発生の進化の歴史を解明 --ゲノム解析による発がんメカニズムの探索--. 京都大学プレスリリース. 2023-07-28.Tracking the ol' mutation trail: Unraveling the long history of breast cancer formation. 京都大学プレスリリース. 2023-08-31.Recent studies have documented frequent evolution of clones carrying common cancer mutations in apparently normal tissues, which are implicated in cancer development1, 2, 3. However, our knowledge is still missing with regard to what additional driver events take place in what order, before one or more of these clones in normal tissues ultimately evolve to cancer. Here, using phylogenetic analyses of multiple microdissected samples from both cancer and non-cancer lesions, we show unique evolutionary histories of breast cancers harbouring der(1;16), a common driver alteration found in roughly 20% of breast cancers. The approximate timing of early evolutionary events was estimated from the mutation rate measured in normal epithelial cells. In der(1;16)(+) cancers, the derivative chromosome was acquired from early puberty to late adolescence, followed by the emergence of a common ancestor by the patient’s early 30s, from which both cancer and non-cancer clones evolved. Replacing the pre-existing mammary epithelium in the following years, these clones occupied a large area within the premenopausal breast tissues by the time of cancer diagnosis. Evolution of multiple independent cancer founders from the non-cancer ancestors was common, contributing to intratumour heterogeneity. The number of driver events did not correlate with histology, suggesting the role of local microenvironments and/or epigenetic driver events. A similar evolutionary pattern was also observed in another case evolving from an AKT1-mutated founder. Taken together, our findings provide new insight into how breast cancer evolves

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research